# Optimal uncertainty quantification for legacy data observations of Lipschitz functions

T. J. Sullivan; M. McKerns; D. Meyer; F. Theil; H. Owhadi; M. Ortiz

- Volume: 47, Issue: 6, page 1657-1689
- ISSN: 0764-583X

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topSullivan, T. J., et al. "Optimal uncertainty quantification for legacy data observations of Lipschitz functions." ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique 47.6 (2013): 1657-1689. <http://eudml.org/doc/273325>.

@article{Sullivan2013,

abstract = {We consider the problem of providing optimal uncertainty quantification (UQ) – and hence rigorous certification – for partially-observed functions. We present a UQ framework within which the observations may be small or large in number, and need not carry information about the probability distribution of the system in operation. The UQ objectives are posed as optimization problems, the solutions of which are optimal bounds on the quantities of interest; we consider two typical settings, namely parameter sensitivities (McDiarmid diameters) and output deviation (or failure) probabilities. The solutions of these optimization problems depend non-trivially (even non-monotonically and discontinuously) upon the specified legacy data. Furthermore, the extreme values are often determined by only a few members of the data set; in our principal physically-motivated example, the bounds are determined by just 2 out of 32 data points, and the remainder carry no information and could be neglected without changing the final answer. We propose an analogue of the simplex algorithm from linear programming that uses these observations to offer efficient and rigorous UQ for high-dimensional systems with high-cardinality legacy data. These findings suggest natural methods for selecting optimal (maximally informative) next experiments.},

author = {Sullivan, T. J., McKerns, M., Meyer, D., Theil, F., Owhadi, H., Ortiz, M.},

journal = {ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique},

keywords = {uncertainty quantification; probability inequalities; non-convex optimization; Lipschitz functions; legacy data; point observations},

language = {eng},

number = {6},

pages = {1657-1689},

publisher = {EDP-Sciences},

title = {Optimal uncertainty quantification for legacy data observations of Lipschitz functions},

url = {http://eudml.org/doc/273325},

volume = {47},

year = {2013},

}

TY - JOUR

AU - Sullivan, T. J.

AU - McKerns, M.

AU - Meyer, D.

AU - Theil, F.

AU - Owhadi, H.

AU - Ortiz, M.

TI - Optimal uncertainty quantification for legacy data observations of Lipschitz functions

JO - ESAIM: Mathematical Modelling and Numerical Analysis - Modélisation Mathématique et Analyse Numérique

PY - 2013

PB - EDP-Sciences

VL - 47

IS - 6

SP - 1657

EP - 1689

AB - We consider the problem of providing optimal uncertainty quantification (UQ) – and hence rigorous certification – for partially-observed functions. We present a UQ framework within which the observations may be small or large in number, and need not carry information about the probability distribution of the system in operation. The UQ objectives are posed as optimization problems, the solutions of which are optimal bounds on the quantities of interest; we consider two typical settings, namely parameter sensitivities (McDiarmid diameters) and output deviation (or failure) probabilities. The solutions of these optimization problems depend non-trivially (even non-monotonically and discontinuously) upon the specified legacy data. Furthermore, the extreme values are often determined by only a few members of the data set; in our principal physically-motivated example, the bounds are determined by just 2 out of 32 data points, and the remainder carry no information and could be neglected without changing the final answer. We propose an analogue of the simplex algorithm from linear programming that uses these observations to offer efficient and rigorous UQ for high-dimensional systems with high-cardinality legacy data. These findings suggest natural methods for selecting optimal (maximally informative) next experiments.

LA - eng

KW - uncertainty quantification; probability inequalities; non-convex optimization; Lipschitz functions; legacy data; point observations

UR - http://eudml.org/doc/273325

ER -

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